Use of contamination-free manufacturing data in fault detection and classification as well as in run-to-run control

ABSTRACT

A method is provided for manufacturing, the method including processing a workpiece in a processing step, detecting defect data after the processing of the workpiece in the processing step has begun and forming an output signal corresponding to at least one type of defect based on the defect data. The method also includes feeding back a control signal based on the output signal to adjust the processing performed in the processing step to reduce the at least one type of defect.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates generally to semiconductor fabricationtechnology, and, more particularly, to a method for manufacturing aworkpiece.

2. Description of the Related Art

There is a constant drive within the semiconductor industry to increasethe quality, reliability and throughput of integrated circuit devices,(e.g., microprocessors, memory devices, and the like. This drive isfueled by consumer demands for higher quality computers and electronicdevices that operate more reliably. These demamnds have resulted in acontinual improvement in the manufacture of semiconductor devices, e.g.,transistors, as well as in the manufacture of integrated circuit devicesincorporating such transistors. Additionally, reducing the defects inthe manufacture of the components of a typical transistor also lowersthe overall cost per transistor as well as the cost of integratedcircuit devices incorporating such transistors.

The technologies underlying semiconductor processing tools haveattracted increased attention over the last several years, resulting insubstantial refinements. However, despite the advances made in thisarea, many of the processing tools that are currently commerciallyavailable suffer certain deficiencies. In particular, such tools; oftenlack advanced process data monitoring capabilities, such as the abilityto provide historical parametric data in a user-friendly format, as wellas event logging, real-time graphical display of both current processingparameters and the processing parameters of the entire run, and remote,ie., local site and worldwide, monitoring. These deficiencies canengender nonoptimal control of critical processing parameters, such asthroughput accuracy, stability and repeatability, processingtemperatures, mechanical tool parameters, and the like. This variabilitymanifests itself as within-run disparities, run-to-run disparities andtool-to-tool disparities that can propagate into deviations in productquality and performance, whereas an ideal monitoring and diagnosticssystem for such tools would provide a mean of monitoring thisvariability, as well as providing means for optimizing control ofcritical parameters.

The present invention is directed to overcoming, or it least reducingthe effects of, one or more of the problems set forth above.

SUMMARY OF THE INVENTION

In one aspect of the present invention, a method is provided formanufacturing, the method including processing a workpiece in aprocessing step, detecting defect data after the processing of theworkpiece in the processing step has begun and forming an output signalcorresponding to at least one type of defect based on the defect data.The method also includes feeding back a control signal based on theoutput signal to adjust the processing performed in the processing stepto reduce the at least one type of defect.

BRIEF DESCRIPTION OF THE, DRAWINGS

The invention may be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, inwhich the leftmost significant digit(s) in the reference numeralsdenote(s) the first figure in which the respective reference numeralsappear, and in which:

FIGS. 1-5 illustrate schematically various embodiments of a method formanufacturing according to the present invention; and

FIGS. 6-10 illustrate schematically various alternative embodiments of amethod for manufacturing according to the present invention.

While the invention is susceptible to various modifications andalternative forms, specific embodiments thereof have been shown by wayof example in the drawings and are herein described in detail. It shouldbe understood, however, that the description herein of specificembodiments is not intended to limit the invention to the particularforms disclosed, but on the contrary, the intention is to cover allmodifications, equivalents, and alternatives failing within the spiritand scope of the invention as defined by the appended claims.

DETAILED DESCRIPTION OF SPECIFIC EMBODIMENTS

Illustrative embodiments of the invention are described below. In theinterest of clarity, not all features of an actual implementation aredescribed in this specification. It will of course be appreciated thatin the development of any such actual embodiment, numerousimplementation-specific decisions must be made to achieve thedevelopers' specific goals, such as compliance with system-related andbusiness-related constraints, which will vary from one implementation toanother. Moreover, it will be appreciated that such a development effortmight be complex and time-consuming, but would nevertheless be a routineundertaking for those of ordinary skill in the art having the benefit ofthis disclosure.

Illustrative embodiments of a method for manufacturing according to thepresent invention are shown in FIGS. 1-10. As shown in FIG. 1, aworkpiece 100, such as a semiconducting substrate or wafer, for example,is delivered to a processing step j 105, where j may have any value fromj=1 to j=N. The total number N of processing steps, such as masking,etching, depositing material and the like, used to form the finishedworkpiece 100, may range from N=1 to about any finite value.

As shown in FIG. 1, the workpiece 100 is sent from the processing step j105 and delivered to an inspection step j 110. In the inspection step j110, the workpiece 100 is inspected to detect data indicative ofdefective processing in the processing step j 105. For example, in theinspection step j 110, the workpiece 100 may be scanned by an inspectiontool (not shown) capable of detecting metal bridges formed betweenfeatures on the workpiece 100, producing scan data 115 indicative ofdefective processing. Additionally, and/or alternatively, in theinspection step j 110, the workpiece 100 may be scanned by an inspectiontool capable of detecting microscratches, ragged polysilicon (poly)lines, blue dots (e.g., small circular defects detected optically,having a blue tint), extra patterns, and the like, formed on theworkpiece 100, again producing scan data 115 indicative of defectiveprocessing.

As shown in FIG. 1, the scan data 115 is sent from the inspection step j110 and delivered to a defect data manipulation step 120. In the defectdata manipulation step 120, the scan data 115 may be manipulated, forexample, by being classified according to the type of defect detected,producing an output signal 125.

As shown in FIG. 1, the output signal 125 is sent from the defect datamanipulation step 120 and delivered to a first defect data display step130. In the first defect data display step 130, the output signal 125may be displayed, for example, by being presented in the form of ahistogram, as illustrated in FIG. 2, showing both the count number(defect counts 135) and the types of defects represented by the outputsignal 125. As shown in FIG. 2, in one illustrative embodiment, thenumber of metal bridges (shown shaded at 200) formed between features onthe workpiece 100 is about 80, in the location scanned, for the durationof the scan. Similarly, the number of microscratches is about 70, thenumber of ragged poly lines is about 50, the number of blue dots isabout 40 and the number of extra patterns is about 60, for example.

The display of the output signal 125 in the first defect data displaystep 130 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 105 to reduce at least onetype of defect detected in the inspection step j 110. The engineer mayalso alter, for example, the classification of the scan data 115, in thedefect data manipulation step 120, according to the type of defectdetected, affecting the output signal 125 produced.

As shown in FIG. 1, the defect counts 135 are sent from the first defectdata display step 130 and delivered to a second defect data display step140. In the second defect data display step 140, the defect counts 135may be displayed, for example, by being presented in the form of agraph, as illustrated in FIG. 3, showing the number of defects/cm²(defect surface density) on the surface of the workpiece 100 plotted asa function of time (measured in seconds). As shown in FIG. 3, in oneillustrative embodiment, the number of metal bridges/cm² formed betweenfeatures on the workpiece 100 eventually crosses a defect surfacedensity threshold 300 (shown in dashed phantom) at a time 305 (shown indotted phantom).

The display of the defect counts 135 in the second defect data displaystep 140 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 105 to reduce at least onetype of defect detected in the inspection step j 110. The engineer mayalso adjust, for example, the defect surface density threshold 300(shown in dashed phantom). The engineer may also select, for example,the type of defect whose defect counts 135 are to be displayed in thesecond defect data display step 140.

As shown in FIG. 1, a feedback control signal 145 is sent from thesecond defect data display step 140 to the processing step j 105 toadjust the processing performed in the processing step j 105 to reduceat least one type of defect detected in the inspection step j 110. Inone illustrative embodiment, as shown in FIG. 3, when the number ofmetal bridges/cm² formed between features crosses the defect surfacedensity threshold 300 (shown in dashed phantom) at the time 305 (shownin dotted phantom), the feedback control signal 145 may act to cause theprocessing performed in the processing step j 105 to increase theoveretch time.

As shown in FIG. 1, the workpiece 100 is sent from the inspection step j110 and delivered to a processing step j+1 150. In the processing stepj+1 150, the workpiece 100 undergoes another one of the total number Nof processing steps, such as masking, etching, depositing material andthe like, used to form the finished workpiece 100. As shown in FIG. 1,the workpiece 100 is then sent from the processing step j+1 150.

As shown in FIG. 4, in another illustrative embodiment, the number ofragged poly lines (shown shaded at 400) formed on the workpiece 100 isabout 50, in the location scanned, for the duration of the scan. Asshown in FIG. 5, in this illustrative embodiment, the number of raggedpoly lines/cm² formed on the workpiece 100 eventually crosses the defectsurface density threshold 500 (shown in dashed phantom) at the time 505(shown in dotted phantom). In this illustrative embodiment, as shown inFIG. 1, the feedback control signal 145 may act to cause the processingperformed in the processing step j 105 to decrease the poly etch time.

As shown in FIGS. 2 and 4, in yet another illustrative embodiment, thenumber of metal bridges (shown shaded at 200 in FIG. 2) formed betweenfeatures on the workpiece 100 is about 80, and the number of ragged polylines (shown shaded at 400 in FIG. 4) formed on the workpiece 100 isabout 50, in the location scanned, for the duration of the scan. Asshown in FIGS. 3 and 5, in this illustrative embodiment, the number ofmetal bridges/cm² formed between features on the workpiece 100eventually crosses the defect surface density threshold 300 (shown indashed phantom in FIG. 3) at the time 305 (shown in dotted phantom inFIG. 3), and the number of ragged poly lines/cm² formed on the workpiece100 eventually crosses the defect surface density threshold 500 (shownin dashed phantom in FIG. 5) at the time 505 (shown in dotted phantom inFIG. 5). In this illustrative embodiment, as shown in FIG. 1, thefeedback control signal 145 may act to cause the processing performed inthe processing step j 105 to both increase the overetch time of themetal lines and decrease the poly etch time, for example, when theprocessing performed in the processing step j 105 permits simultaneous,and yet selective, etching of both the metal lines and the poly.

In one illustrative embodiment, in both the first and second defect datadisplay steps 130 and 140, the engineer may be provided with advancedprocess data monitoring capabilities, such as the ability to providehistorical parametric data in a user-friendly format, as well as eventlogging, real-time graphical display of both current processingparameters and the processing parameters of the entire run, and remote,i.e., local site and worldwide, monitoring. These capabilities mayengender more optimal control of critical processing parameters, such asthroughput accuracy, stability and repeatability, processingtemperatures, mechanical tool parameters, and the like. This moreoptimal control of critical processing parameters reduces thisvariability. This reduction in variability manifests itself as fewerwithin-run disparities, fewer run-to-run disparities and fewertool-to-tool disparities. This reduction in the number of thesedisparities that can propagate means fewer deviations in product qualityand performance. In such an illustrative embodiment of a method ofmanufacturing according to the present invention, a monitoring anddiagnostics system may be provided that monitors this variability andoptimizes control of critical parameters.

As shown in FIG. 6, a workpiece 600, such as a semiconducting substrateor wafer, for example, is delivered to a processing step j 605, where jmay have any value from j=1 to j=N. The total number N of processingsteps, such as masking, etching, depositing material and the like, usedto form the finished workpiece 600, may range from N=1 to about anyfinite value.

As shown in FIG. 6, the workpiece 600 is sent from the processing step j605 and delivered to an inspection step j 610. In the inspection step j610, the workpiece 600 is inspected to detect data indicative ofdefective processing in the processing step j 605. For example, in theinspection step j 610, the workpiece 600 may be scanned by an inspectiontool capable of detecting metal bridges formed between features on theworkpiece 600, producing scan data 615 indicative of defectiveprocessing. Additionally, and/or alternatively, in the inspection step j610, the workpiece 100 may be scanned by an inspection tool capable ofdetecting microscratches, ragged polysilicon (poly) lines, blue dots,extra patterns, and the like, formed on the workpiece 600, againproducing scan data 615 indicative of defective processing.

As shown in FIG. 6, the scan data 615 is sent from the inspection step j610 and delivered to a defect data manipulation step 620. In the defectdata manipulation step 620, the scan data 615 may be manipulated, forexample, by being classified according to the type of defect detected,producing the output signal 625.

As shown in FIG. 6, the output signal 625 is sent from the defect datamanipulation step 620 and delivered to a first defect data display step630. In the first defect data display step 630, the output signal 625may be displayed, for example, by being presented in the form of ahistogram, as illustrated in FIGS. 2 and 4, showing both the countnumber (defect counts 645) and the types of defects represented by theoutput signal 625. As shown in FIG. 2, in one illustrative embodiment,the number of metal bridges (shown shaded at 200) formed betweenfeatures on the workpiece 100 is about 80, in the location scanned, forthe duration of the scan. Similarly, as shown in FIG. 4, the number ofragged poly lines (shown shaded at 400) is about 50, in the locationscanned, for the duration of the scan.

The display of the output signal 625 in the first defect data displaystep 630 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 605 to reduce at least onetype of defect detected in the inspection step j 610. The engineer mayalso alter, for example, the classification of the scan data 615, in thedefect data manipulation step 620, according to the type of defectdetected, affecting the output signal 625 produced.

As shown in FIG. 6, a feedback control signal 635 is sent from the firstdefect data display step 630 to the processing step j 605 to adjust theprocessing performed in the processing step j 605 to reduce at least onetype of defect detected in the inspection step j 610. In oneillustrative embodiment, as shown in FIG. 2, when the number of metalbridges (shown shaded at 200) formed between features on the workpiece600 exceeds a predetermined value, for example, about 80, the feedbackcontrol signal 635 may act to cause the processing performed in theprocessing step j 605 to increase the overetch time. In anotherillustrative embodiment, as shown in FIG. 4, when the number of raggedpoly lines (shown shaded at 400) formed on the workpiece 600 exceeds apredetermined value, for example, about 50, the feedback control signal635 may act to cause the processing performed in the processing step j605 to decrease the poly etch time.

As shown in FIG. 6, the workpiece 600 is sent from the inspection step j610 and delivered to a processing step j+1 640. In the processing stepj+1 640, the workpiece 600 undergoes another one of the total number Nof processing steps, such as masking, etching, depositing material andthe like, used to form the finished workpiece 600. As shown in FIG. 6,the workpiece 600 is then sent from the processing step j+1 640.

As shown in FIG. 6, in addition to, and/or instead of, the feedbackcontrol signal 635, the defect counts 645 may be sent from the firstdefect data display step 630 and may then be delivered to a seconddefect data display step 650. In the second defect data display step650, the defect counts 645 may be displayed, for example, by beingpresented in the form of a graph, as illustrated in FIG. 3, showing thenumber of defects/cm² (defect surface density) on the surface of theworkpiece 600 plotted as a function of time (measured in seconds). Asshown in FIG. 3, in one illustrative embodiment, the number of metalbridges/cm² formed between features on the workpiece 600 may eventuallycross the defect surface density threshold 300 (shown in dashed phantom)at the time 305 (shown in dotted phantom).

The display of the defect counts 645 in the second defect data displaystep 650 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 605 to reduce at least onetype of defect detected in the inspection step j 610. The engineer mayalso adjust, for example, the defect surface density threshold 300(shown in dashed phantom). The engineer may also select, for example,the type of defect whose defect counts 645 are to be displayed in thesecond defect data display step 650.

As shown in FIG. 6, a feedback control signal 655 may be sent from thesecond defect data display step 650 to the processing step j 605 toadjust the processing performed in the processing step j 605 to reduceat least one type of defect detected in the inspection step j 610. Inone illustrative embodiment, as shown in FIG. 3, when the number ofmetal bridges/cm² formed between features crosses the defect surfacedensity threshold 300 (shown in dashed phantom) at the time 305 (shownin dotted phantom), the feedback control signal 655 may act to cause theprocessing performed in the processing step j 605 to increase theoveretch time.

As shown in FIG. 5, in another illustrative embodiment, the number ofragged poly lines/cm² formed on the workpiece 600 may eventually crossthe defect surface density threshold 500 (shown in dashed phantom) atthe time 505 (shown in dotted phantom). In this illustrative embodiment,the feedback control signal 655 may act to cause the processingperformed in the processing step j 605 to decrease the poly etch time.

In one illustrative embodiment, in both the first and second defect datadisplay steps 630 and 650, the engineer may be provided with advancedprocess data monitoring capabilities, such as the ability to providehistorical parametric data in a user-friendly format, as well as eventlogging, real-time graphical display of both current processingparameters and the processing parameters of the entire run, and remote,i.e., local site and worldwide, monitoring. These capabilities mayengender more optimal control of critical processing parameters, such asthroughput accuracy, stability and repeatability, processingtemperatures, mechanical tool parameters, and the like. This moreoptimal control of critical processing parameters reduces thisvariability. This reduction in variability manifests itself as fewerwithin-run disparities, fewer run-to-run disparities and fewertool-to-tool disparities. This reduction in the number of thesedisparities that can propagate means fewer deviations in product qualityand performance. In such an illustrative embodiment of a method ofmanufacturing according to the present invention, a monitoring anddiagnostics system may be provided that monitors this variability andoptimizes control of critical parameters.

As shown in FIG. 7, a workpiece 700, such as a semiconducting substrateor wafer, for example, is delivered to a processing step j 705, where jmay have any value from j=1 to j=1 N. The total number N of processingsteps, such as masking, etching, depositing material and the like, usedto form the finished workpiece 700, may range from N=1 to about anyfinite value.

As shown in FIG. 7, the workpiece 700 is sensed and/or scanned by an insitu sensor or monitor (not shown) in the processing step j 705 todetect data indicative of defective processing, and/or defectiveprocessing conditions, after the processing has begun in the processingstep j 705. For example, in the processing step j 705, the workpiece 700may be sensed and/or scanned by in situ sensors or monitors capable ofdetecting metal bridges formed between features on the workpiece 700,producing in situ sensor data 710 indicative of defective processing,and/or defective processing conditions. Additionally, and/oralternatively, in the processing step j 705, the workpiece 700 may besensed and/or scanned by in situ sensors capable of detecting largeparticles in the processing chamber, microscratches, ragged polysilicon(poly) lines, blue dots, extra patterns, and the like, formed on theworkpiece 700, again producing in situ sensor data 710 indicative ofdefective processing, and/or defective processing conditions.Additionally, and/or alternatively, the output/exhaust (not shown) ofthe tool doing the processing (not shown) in the processing step j 705may be sensed and/or scanned by in situ sensors capable of detecting andmeasuring gas particles in the processing chamber over time, and thelike, again producing in situ sensor data 710 indicative of changes inthe actual processing, and/or defective processing, and/or defectiveprocessing conditions.

As shown in FIG. 7, the in situ sensor data 710 is sent from theprocessing step j 705 and delivered to a defect data manipulation step715. In the defect data manipulation step 715, the in situ sensor data710 may be manipulated, for example, by being classified according tothe type of defect detected, producing output signal 720.

As shown in FIG. 7, the output signal 720 is sent from the defect datamanipulation step 715 and delivered to a first defect data display step725. In the first defect data display step 725, the output signal 720may be displayed, for example, by being presented in the form of ahistogram, as illustrated in FIG. 8, showing both the count number(defect counts 730) and the types of defects represented by the outputsignal 720. As shown in FIG. 8, in one illustrative embodiment, thenumber of large particles in the processing chamber (shown shaded at800) is about 100, in the location scanned, for the duration of thescan. Similarly, the number of metal bridges is about 90, the number ofmicroscratches is about 80, the number of ragged poly lines is about 70,the number of blue dots is about 50 and the number of extra patterns isabout 70, for example.

The display of the output signal 720 in the first defect data displaystep 725 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 705 to reduce at least onetype of defect sensed and/or scanned by the in situ sensors in theprocessing step j 705. The engineer may also alter, for example, theclassification of the in situ sensor data 710, in the defect datamanipulation step 715, according to the type of defect detected,affecting the output signal 720 produced.

As shown in FIG. 7, the defect counts 730 are sent from the first defectdata display step 725 and delivered to a second defect data display step735. In the second defect data display step 735, the defect counts 730may be displayed, for example, by being presented in the form of agraph, as illustrated in FIG. 9, showing the number of defects sensedand/or scanned by the in situ sensors in the processing step j 705plotted as a function of time (measured in seconds). As shown in FIG. 9,in one illustrative embodiment, the number of large particles in theprocessing chamber eventually crosses a large particle count threshold900 (shown in dashed phantom) at a time 905 (shown in dotted phantom).

The display of the defect counts 730 in the second defect data displaystep 735 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 705 to reduce at least onetype of defect sensed and/or scanned by the in situ sensors in theprocessing step j 705. The engineer may also adjust, for example, thedefect surface density threshold 300 (shown in dashed phantom). Theengineer may also select, for example, the type of defect whose defectcounts 730 are to be displayed in the second defect data display step735.

As shown in FIG. 7, a feedback control signal 740 is sent from thesecond defect data display step 735 to the processing step j 705 toadjust the processing performed in the processing step j 705 to reduceat least one type of defect detected, by being sensed and/or scanned bythe in situ sensors, for example, in the processing step j 705. In oneillustrative embodiment, as shown in FIG. 9, when the number of largeparticles in the processing chamber crosses the large particle countthreshold 900 (shown in dashed phantom) at the time 905 (shown in dottedphantom), the feedback control signal 740 may act to cause theprocessing performed in the processing step j 705 to do chamber pastingand/or initiate chamber conditioning, for example.

As shown in FIG. 7, the workpiece 700 is sent from the processing step j705 and delivered to a processing step j+1 745. In the processing stepj+1 745, the workpiece 700 undergoes another one of the total number Nof processing steps, such as masking, etching, depositing material andthe like, used to form the finished workpiece 700. As shown in FIG. 7,the workpiece 700 is then sent from the processing step j+1 745. Afterthe processing has begun in the processing step j+1 745, the workpiece700 may be sensed and/or scanned by in situ sensors in the processingstep j+1 745 to detect data indicative of defective processing, and/ordefective processing conditions, as described above.

As shown in FIG. 2, in another illustrative embodiment, the number ofmetal bridges (shown shaded at 200) formed between features on theworkpiece 700 is about 80, in the location scanned, for the duration ofthe scan. As shown in FIG. 3, in this illustrative embodiment, thenumber of metal bridges/cm² formed between features on the workpiece 700eventually crosses the defect surface density threshold 300 (shown indashed phantom) at the time 305 (shown in dotted phantom). In thisillustrative embodiment, as shown in FIG. 7, the feedback control signal740 may act to cause the processing performed in the processing step j705 to increase the overetch time.

As shown in FIG. 4, in yet another illustrative embodiment, the numberof ragged poly lines (shown shaded at 400) formed on the workpiece 100is about 50, in the location scanned, for the duration of the scan. Asshown in FIG. 5, in this illustrative embodiment, the number of raggedpoly lines/cm² formed on the workpiece 100 eventually crosses the defectsurface density threshold 500 (shown in dashed phantom) at the time 505(shown in dotted phantom). In this illustrative embodiment, as shown inFIG. 7, the feedback control signal 740 may act to cause the processingperformed in the processing step j 705 to decrease the poly etch time.

In one illustrative embodiment, in both the first and second defect datadisplay steps 725 and 735, the engineer may be provided with advancedprocess data monitoring capabilities, such as the ability to providehistorical parametric data in a user-friendly format, as well as eventlogging, real-time graphical display of both current processingparameters and the processing parameters of the entire run, and remote,ie., local site and worldwide, monitoring. These capabilities mayengender more optimal control of critical processing parameters, such asthroughput accuracy, stability and repeatability, processingtemperatures, mechanical tool parameters, and the like. This moreoptimal control of critical processing parameters reduces thisvariability. This reduction in variability manifests itself as fewerwithin-run disparities, fewer run-to-run disparities and fewertool-to-tool disparities. This reduction in the number of thesedisparities that can propagate means fewer deviations in product qualityand performance. In such an illustrative embodiment of a method ofmanufacturing according to the present invention, a monitoring anddiagnostics system may be provided that monitors this variability andoptimizes control of critical parameters.

As shown in FIG. 10, a workpiece 1000, such as a semiconductingsubstrate or wafer, for example, is delivered to a processing step j1005, where j may have any value from j=1 to j=N. The total number N ofprocessing steps, such as masking, etching, depositing material and thelike, used to form the finished workpiece 1000, may range from N=1 toabout any finite value.

As shown in FIG. 10, the workpiece 1000 is sensed and/or scanned by insitu sensors (not shown) in the processing step j 1005 to detect dataindicative of defective processing, and/or defective processingconditions, after the processing has begun in the processing step j1005. For example, in the, processing step j 1005, the workpiece 1000may be sensed and/or scanned by in situ sensors capable of detectingmetal bridges formed between features on the workpiece 1000, producingin situ sensor data 1010 indicative of defective processing, and/ordefective processing conditions. Additionally, and/or alternatively, inthe processing step j 1005, the workpiece 1000 may be sensed and/orscanned by in situ sensors capable of detecting large particles in theprocessing chamber, microscratches, ragged polysilicon (poly) lines,blue dots, extra patterns, and the like, formed on the workpiece 1000,again producing the in situ sensor data 1010 indicative of defectiveprocessing, and/or defective processing conditions.

As shown in FIG. 10, the in situ sensor data 1010 is sent from theprocessing step j 1005 and delivered to a defect data manipulation step1015. In the defect data manipulation step 1015, the in situ sensor data1010 may be manipulated, for example, by being classified according tothe type of defect detected, producing the output signal 1020.

As shown in FIG. 10, the output signal 1020 is sent from the defect datamanipulation step 1015 and delivered to a first defect data display step1025. In the first defect data display step 1025, the output signal 1020may be displayed, for example, by being presented in the form of ahistogram, as illustrated in FIGS. 2, 4 and 8, showing both the countnumber (defect counts 1040) and the types of defects represented by theoutput signal 1020. As shown in FIG. 2, in one illustrative embodiment,the number of metal bridges (shown shaded at 200) formed betweenfeatures on the workpiece 1000 is about 80, in the location scanned, forthe duration of the scan. Similarly, as shown in FIG. 4, in anotherillustrative embodiment, the number of ragged poly lines (shown shadedat 400) is about 50, in the location scanned, for the duration of thescan. Likewise, as shown in FIG. 8, in yet another illustrativeembodiment, the number of large particles in the processing chamber(shown shaded at 800) is about 100, in the location scanned, for theduration of the scan.

The display of the output signal 1020 in the first defect data displaystep 1025 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 1005 to reduce at leastone type of defect sensed and/or scanned by the in situ sensors in theprocessing step j 1005. The engineer may also alter, for example, theclassification of the in situ sensor data 1010, in the defect datamanipulation step 1015, according to the type of defect detected,affecting the output signal 1020 produced.

As shown in FIG. 10, a feedback control signal 1030 is sent from thefirst defect data display step 1025 to the processing step j 1005 toadjust the processing performed in the processing step j 1005 to reduceat least one type of defect detected, by being sensed and/or scanned bythe in situ sensors, for example, in the processing step j 1005. In oneillustrative embodiment, as shown in FIG. 8, when the number of largeparticles (shown shaded at 800) exceeds a predetermined value, forexample, about 100, the feedback control signal 1030 may act to causethe processing performed in the processing step j 1005 to do chamberpasting and/or initiate chamber conditioning, for example.

In another illustrative embodiment, as shown in FIG. 2, when the numberof metal bridges (shown shaded at 200) formed between features on theworkpiece 1000 exceeds a predetermined value, for example, about 80, thefeedback control signal 1030 may act to cause the processing performedin the processing step j 1005 to increase the overetch time. In yetanother illustrative embodiment, as shown in FIG. 4, when the number ofragged poly lines (shown shaded at 400) formed on the workpiece 1000exceeds a predetermined value, for example, about 50, the feedbackcontrol signal 1030 may act to cause the processing performed in theprocessing step j 1005 to decrease the poly etch time.

As shown in FIG. 10, the workpiece 1000 is sent from the processing stepj 1005 and delivered to a processing step j+1 1035. In the processingstep j+1 1035, the workpiece 1000 undergoes another one of the totalnumber N of processing steps, such as masking, etching, depositingmaterial and the like, used to form the finished workpiece 1000.

As shown in FIG. 10, the workpiece 1000 is then sent from the processingstep j+1 1035. After the processing has begun in the processing step j+11035, the workpiece 1000 may be sensed and/or scanned by in situ sensorsin the processing step j+1 1035 to detect data indicative of defectiveprocessing, and/or defective processing conditions, as described above.

As shown in FIG. 10, in addition to, and/or instead of, the feedbackcontrol signal 1030, the defect counts 1040 may be sent from the firstdefect data display step 1025 and may then be delivered to a seconddefect data display step 1045. In the second defect data display step1045, the defect counts 1040 may be displayed, for example, by beingpresented in the form of a graph, as illustrated in FIG. 9, showing thenumber of defects sensed and/or scanned by the in situ sensors in theprocessing step j 1005 plotted as a function of time (measured inseconds). As shown in FIG. 9, in one illustrative embodiment, the numberof large particles in the processing chamber eventually crosses thelarge particle count threshold 900 (shown in dashed phantom) at the time905 (shown in dotted phantom).

The display of the defect counts 1040 in the second defect data displaystep 1045 may be used to alert an engineer of the need to adjust theprocessing performed in the processing step j 1005 to reduce at leastone type of defect sensed and/or scanned by the in situ sensors in theprocessing step j 1005. The engineer may also adjust, (for example, thedefect surface density threshold 300 (shown in dashed phantom). Theengineer may also select, for example, the type of defect whose defectcounts 1040 are to be displayed in the second defect data display step1045.

As shown in FIG. 10, a feedback control signal 1050 may be sent from thesecond defect data display step 1045 to the processing step j 1005 toadjust the processing performed in the processing step j 1005 to reduceat least one type of defect detected, by being sensed and/or scanned bythe in situ sensors, for example, in the processing step j 1005. In oneillustrative embodiment, as shown in FIG. 9, the feedback control signal1050 may act to cause the processing performed in the processing step j1005 to do chamber pasting and/or initiate chamber conditioning, forexample.

In another illustrative embodiment, as shown in FIG. 3, when the numberof metal bridges/cm² formed between features crosses the defect surfacedensity threshold 300 (shown in dashed phantom) at the time 305 (shownin dotted phantom), the feedback control signal 1050 may act to causethe processing performed in the processing step j 1005 to increase theoveretch time.

As shown in FIG. 5, in yet another illustrative embodiment, the numberof ragged poly lines/cm² formed on the workpiece 1000 may eventuallycross the defect surface density threshold 500 (shown in dashed phantom)at the time 505 (shown in dotted phantom). In this illustrativeembodiment, the feedback control signal 1050 may act to cause theprocessing performed in the processing step j 1005 to decrease the polyetch time.

In one illustrative embodiment, in both the first and second defect datadisplay steps 1025 and 1045, the engineer may be provided with advancedprocess data monitoring capabilities, such as the ability to providehistorical parametric data in a user-friendly format, as well as eventlogging, real-time graphical display of both current processingparameters and the processing parameters of the entire run, and remote,i.e., local site and worldwide, monitoring. These capabilities mayengender more optimal control of critical processing parameters, such asthroughput accuracy, stability and repeatability, processingtemperatures, mechanical tool parameters, and the like. This moreoptimal control of critical processing parameters reduces thisvariability. This reduction in variability manifests itself as fewerwithin-run disparities, fewer run-to-run disparities and fewertool-to-tool disparities. This reduction in the number of thesedisparities that can propagate means fewer deviations in product qualityand performance. In such an illustrative embodiment of a method ofmanufacturing according to the present invention, a monitoring anddiagnostics system may be provided that monitors this variability andoptimizes control of critical parameters.

Any of the above-disclosed embodiments of a method of manufacturingaccording to the present invention enables the use of defect datasignals sent from an inspection tool to make real-time processing tooladjustments, either manually and/or automatically, to improve and/orbetter control the yield. This defect detection may be downstream fromthe processing step (see, for example, FIGS. 1 and 6), or, alternativelyand/or additionally, may be performed in situ (see, for example, FIGS. 7and 10). Additionally, any of the above-disclosed embodiments of amethod of manufacturing according to the present invention enablessemiconductor device fabrication with increased device density andprecision and enable a streamlined and simplified process flow, therebydecreasing the complexity and lowering the costs of the manufacturingprocess and increasing throughput.

The particular embodiments disclosed above are illustrative only, as theinvention may be modified and practiced in different but equivalentmanners apparent to those skilled in the art having the benefit of theteachings herein. Furthermore, no limitations are intended to thedetails of construction or design herein shown, other than as describedin the claims below. It is therefore evident that the particularembodiments disclosed above may be altered or modified and all suchvariations are considered within the scope and spirit of the invention.Accordingly, the protection sought herein is as set forth in the claimsbelow.

What is claimed is:
 1. A method of manufacturing, the method comprising:processing a workpiece in a processing step; detecting defect data afterthe processing of the workpiece in the processing step has begun;forming an output signal corresponding to at least one type of defectbased on the defect data, wherein the formation of the output signalincludes counting the number of the at least one type of defect anddetermining a surface density of the at least one type of defect as afunction of time; and feeding back a control signal based on the outputsignal to adjust the processing performed in the processing step toreduce the at least one type of defect.
 2. The method of claim 1,wherein the feeding back of the control signal based on the outputsignal includes feeding back the control signal when the number of theat least one type of defect is at least about a first predeterminedvalue.
 3. The method of claim 2, wherein the feeding back of the controlsignal based on the output signal further includes feeding back thecontrol signal when the surface density of the at least one type ofdefect is at least about a second predetermined value.
 4. The method ofclaim 1, wherein the feeding back of the control signal based on theoutput signal includes feeding back the control signal when the surfacedensity of the at least one type of defect is at least about a secondpredetermined value.
 5. A method of manufacturing, the methodcomprising: processing a first workpiece in a processing step; detectingat least one type of defect in an inspection step after the processingof the first workpiece in the processing step; forming an output signalcorresponding to at least one type of detected defect, wherein theformation of the output signal includes counting the number of the atleast one type of defect and determining a surface density of the atleast one type of defect as a function of time; and feeding back acontrol signal based on the output signal to adjust the processingperformed in the processing step on a second workpiece to reduce the atleast one type of defect.
 6. The method of claim 5, wherein the feedingback of the control signal based on the output signal includes feedingback the control signal when the number of the at least one type ofdefect is at least about a first predetermined value.
 7. The method ofclaim 5, wherein the feeding back of the control signal based on theoutput signal further includes feeding back the control signal when thesurface density of the at least one type of defect is at least about asecond predetermined value.
 8. A method of manufacturing, the methodcomprising: processing a workpiece in a processing step; detectingdefect data using an in situ sensor after the processing of theworkpiece in the processing step has begun; forming an output signalcorresponding to at least one type of defect based on the defect data,wherein the formation of the output signal includes counting the numberof the at least one type of defect and determining a surface density ofthe at least one type of defect as a function of time; and feeding backa control signal based on the output signal to adjust the processingperformed on the workpiece in the processing step to reduce the at leastone type of defect.
 9. The method of claim 8, wherein the feeding backof the control signal based on the output signal includes feeding backthe control signal when the number of the at least one type of defect isat least about a first predetermined value.
 10. The method of claim 8,wherein the feeding back of the control signal based on the outputsignal further includes feeding back the control signal when the surfacedensity of the at least one type of defect is at least about a secondpredetermined value.
 11. A method of manufacturing, the methodcomprising: processing a workpiece in a processing step; detectingdefect data after the processing of the workpiece in the processing stephas begun; forming an output signal corresponding to at least one typeof defect based on the defect data, wherein the formation of the outputsignal includes counting the number of the at least one type of defect,determining a surface density of the at least one type of defect as afunction of time, counting the number of a second type of defectdifferent than the at least one type of defect and determining a surfacedensity of the second type of defect as a function of time; and feedingback a control signal when the number of the at least one type of defectis at least about a first predetermined value, when the surface densityof the at least one type of defect is at least about a secondpredetermined value, when the number of the second type of defect is atleast about a third predetermined value and when the surface density ofthe second type of defect is at least about a fourth predetermined valueto adjust the processing performed in the processing step to reduce theat least one type of defect.
 12. A method of manufacturing, the methodcomprising: processing a first workpiece in a processing step; detectingat least one type of defect in an inspection step after the processingof the first workpiece in the processing step; forming an output signalcorresponding to at least one type of detected defect, wherein theformation of the output signal includes counting the number of the atleast one type of defect, determining a surface density of the at leastone type of defect as a function of time, counting the number of asecond type of defect different than the at least one type of defect anddetermining a surface density of the second type of defect as a functionof time; and feeding back a control signal when the number of the atleast one type of defect is at least about a first predetermined value,when the surface density of the at least one type of defect is at leastabout a second predetermined value, when the number of the second typeof defect is at least about a third predetermined value and when thesurface density of the second type of defect is at least about a fourthpredetermined value to adjust the processing performed in the processingstep on a second workpiece to reduce the at least one type of defect.13. A method of manufacturing, the method comprising: processing aworkpiece in a processing step; detecting defect data using an in situsensor after the processing of the workpiece in the processing step hasbegun; forming an output signal corresponding to at least one type ofdefect based on the defect data, wherein the formation of the outputsignal includes counting the number of the at least one type of defect,determining a surface density of the at least one type of defect as afunction of time, counting the number of a second type of defectdifferent than the at least one type of defect and determining a surfacedensity of the second type of defect as a function of time; and feedingback a control signal when the number of the at least one type of defectis at least about a first predetermined value, when the surface densityof the at least one type of defect is at least about a secondpredetermined value, when the number of the second type of defect is atleast about a third predetermined value and when the surface density ofthe second type of defect is at least about a fourth predetermined valueto adjust the processing performed on the workpiece in the processingstep to reduce the at least one type of defect.